Healthcare and finance organizations face a paradox: they need massive, realistic datasets to build reliable AI, but real data carries legal risks. HIPAA and GDPR protect sensitive information. Scrubbing data removes nuance. Teams often hit a wall where they cannot collect enough high-quality labeled examples without violating compliance rules or exhausting internal sources.
Real risk: data breaches and regulatory fines
A single leaked dataset can trigger multi-million dollar penalties. In 2023, a U.S. hospital system paid $4.3 million for a ransomware attack that exposed patient names, dates of birth, and medical record numbers. Financial firms face similar consequences when transaction histories or account balances are stolen. Synthetic data replaces real records with mathematically generated counterparts that preserve statistical properties but contain no actual personal identifiers.
Synthetic data preserves statistical patterns
High quality synthetic datasets retain the statistical behavior of the original data. Studies in healthcare show that models trained on synthetic EHR features achieve F1 scores within 2 to 3 percentage points of models trained on the real data. In finance, synthetic transaction logs maintain correlation structures across products and timeframes. The key is accurate modeling of joint distributions, not just marginal distributions. Techniques like variational autoencoders and normalizing flows have become standard for capturing multivariate dependencies, especially where variables are highly correlated, such as credit scores and loan delinquency risk.
Tradeoffs to consider
- Dataset size: synthetic data can be generated at virtually any scale, but initial creation requires careful modeling and validation.
- Edge cases and rare events: rare pathologies or unusual fraud patterns may not appear in the initial synthetic set and need targeted augmentation.
- Domain expertise: synthetic data must be validated by domain experts to ensure it reflects real workflows and terminology.
- Regulatory acceptance: organizations must document that synthetic data meets legal standards and does not inadvertently leak protected information.
The most promising synthetic data pipelines combine advanced modeling with domain validation to produce datasets that scale infinitely without exposing real records.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This capability helps teams generate synthetic datasets that reflect how people actually work with software, such as navigating electronic health record systems or accessing banking portals. Coasty can produce custom synthetic trajectories and examples tailored to specific use cases. The service is custom and contact-led, meaning you discuss your requirements directly with the Coasty data team to design a solution that fits your constraints and compliance needs.
If you need synthetic data for healthcare or finance AI that protects privacy and scales with your growth, the next step is to book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .
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